This package provides a package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).

Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionality for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well.

This package provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments.

This is a package for segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.

This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels.

This package provides tools for calculating the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data.

This package provides mappings from Entrez gene identifiers to various annotations for the genome of the model fruit fly Drosophila melanogaster.

This package provides S4 data structures and basic functions to deal with flow cytometry data.

BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.

This package provides tools for the computationally efficient analysis of quantitative trait loci (QTL) data, including eQTL, mQTL, dsQTL, etc. The software in this package aims to support refinements and functional interpretation of members of a collection of association statistics on a family of feature/genome hypotheses.

This package performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

This is a package for biclustering analysis and exploration of results.

rtracklayer is an extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport.

This package identifies differential expression in high-throughput count data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.

This package implements algorithms for calculating microarray enrichment (ACME), and it is a set of tools for analysing tiling array of combined chromatin immunoprecipitation with DNA microarray (ChIP/chip), DNAse hypersensitivity, or other experiments that result in regions of the genome showing enrichment. It does not rely on a specific array technology (although the array should be a tiling array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory.

The project is intended to support the use of sequins(synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard library for quantitative analysis, modelling, and visualization of spike-in controls.

This package implements a method that aims to identify enhancers on large scale. The STARR-seq data consists of two sequencing datasets of the same targets in a specific genome. The input sequences show which regions where tested for enhancers. Significant enriched peaks i.e. a lot more sequences in one region than in the input where enhancers in the genomic DNA are, can be identified. So the approach pursued is to call peak every region in which there is a lot more (significant in a binomial model) STARR-seq signal than input signal and propose an enhancer at that very same position. Enhancers then are called weak or strong dependent of there degree of enrichment in comparison to input.

This is a package for significance analysis of Microarrays for differential expression analysis, RNAseq data and related problems.

MethylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from Reduced representation bisulfite sequencing (RRBS) and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq.

This package provides a set of annotation maps describing the entire Disease Ontology.

This package provides an interface to implementations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.

This package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms.

This package provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples.